Before digging into the topic let's briefly set the scene by providing some background context. Background In 2026-Q1 I decided to invest into a custom pc to run local AI Models. Being an IT Professional I grew increasingly concerned seeing the pc component shift heavily towards AI Business cases. PC component prices was increasing rapidly and high-end components becoming scarce or non-available. Given the overall AI development in general I realized I wanted to ensure I owned AI capability at home, and that if I want to stay current in my career I need to dive in head first into AI and AI Development. A powerful driver and motivator is that I want to see just how far I can take AI capabilities based on localhosted opensource methodology over paying a subscription/pay as you go solutions. I eventually came across Clief's YouTube videos which got me curious about ICM and I decided to join this community. Getting educated Working through the Classroom Foundational course I noticed that ICM is primarily used together with Claude. Reading Clief's thesis ( Interpretable Context Methodology: Folder Structure as Agentic Architecture - https://arxiv.org/abs/2603.16021 ) strengthened my own observation that ICM conceptually is model and tooling agnostic. As long as the AI Model can access and interpret the ICM specification files it shouldn't matter if the model itself is run on cloud services like Claude, Gemini etc. or local hosted alternatives. I decided to put this to the test and do it practically. Working through The Foundation: Module 1.2 ( https://www.skool.com/cliefnotes/classroom/036893d9?md=fdee3f73c53b46049078494c0cfb2e54 ) where we create our first ICM folder and get a quick win. I decided that since the Module 1.2 usecase is easy to start with, it makes sense to get it set up running in my own localhosted ai environment. I also wanted to proceed with a real usecase where ICM can be helpful.

Giving away this tool I built for myself to transcribe meetings for later ingestion into my project management workflow. This is peasant stuff compared to what @Ari Evergreen has been releasing latelly in the dictation space but with her help I built something nice and compact for myself: an ever-present monitor ready to be invoked to transcribe meetings. Simply set your hotkey, record your meeting, enter any additional info you want to be added, and have your transcription output to a folder of your choice as an md file ready to be integrated into your ingestion workflow. Use it to record meetings or even just notes to yourself. Everything runs off a local whisper model and python and never leaves your computer. Interested? Check out the repo to try yourself: https://github.com/donroy26/meeting-transcriber